package orf

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package orf

orf
- README
- Library orf
  - Orf
    
    RFC
    
    IntMap
    
    IntSet
    
    RFR
    
    IntMap
    
    Utls
- Sources
  - orf
    
    RFC.ml
    
    RFR.ml
    
    orf.ml

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Module `Orf.RFR`Source

Sourcemodule IntMap = BatMap.Int

Sourcetype features = int IntMap.t

Sourcetype dep_var = float

Sourcetype sample = features * dep_var

Sourcetype tree =

| Leaf of dep_var
| Node of tree * int * int * tree

Sourcetype metric =

| MSE
| MAE
| MAD

Sourcetype forest = (tree * int array) array

Source

val train : 
  int ->
  Random.State.t ->
  metric ->
  int ->
  RFC.int_or_float ->
  int ->
  RFC.int_or_float ->
  int ->
  sample array ->
  forest

train ncores rng metric ntrees max_features card_features max_samples min_node_size training_set

Sourceval predict_one : int -> forest -> sample -> dep_var * float

(pred_avg, pred_std_dev) = predict_one ncores trained_forest sample

Sourceval predict_many : int -> forest -> sample array -> (dep_var * float) array

like predict_one but for an array of samples

Sourceval predict_OOB : forest -> sample array -> (dep_var * dep_var) array

use a trained forest to predict on the Out Of Bag (OOB) training set of each tree. The training_set must be provided in the same order than when the model was trained. Can be used to get a reliable model performance estimate, even if you don't have a left out test set. truth_preds = predict_OOB forest training_set

Sourceval r2 : (dep_var * dep_var) array -> float

r2 truth_preds: coefficient of determination R^2

Sourceval drop_OOB : forest -> forest

make trained model forget OOB samples (reduce model size)

Sourcetype filename = string

Sourceval save : filename -> forest -> unit

Save model to file (Marshal). OOB samples are dropped prior to saving the model.

Sourceval restore : filename -> forest

Restore model from file (Marshal)

package orf

Module Orf.RFRSource

Module `Orf.RFR`Source